class sklearn.gaussian_process.kernels.RBF(length_scale=1.0, length_scale_bounds=(1e05, 100000.0))
[source]
Radialbasis function kernel (aka squaredexponential kernel).
The RBF kernel is a stationary kernel. It is also known as the “squared exponential” kernel. It is parameterized by a lengthscale parameter length_scale>0, which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs X (anisotropic variant of the kernel). The kernel is given by:
k(x_i, x_j) = exp(1 / 2 d(x_i / length_scale, x_j / length_scale)^2)
This kernel is infinitely differentiable, which implies that GPs with this kernel as covariance function have mean square derivatives of all orders, and are thus very smooth.
New in version 0.18.
Parameters: 


Attributes: 

__call__ (X[, Y, eval_gradient])  Return the kernel k(X, Y) and optionally its gradient. 
clone_with_theta (theta)  Returns a clone of self with given hyperparameters theta. 
diag (X)  Returns the diagonal of the kernel k(X, X). 
get_params ([deep])  Get parameters of this kernel. 
is_stationary ()  Returns whether the kernel is stationary. 
set_params (**params)  Set the parameters of this kernel. 
__init__(length_scale=1.0, length_scale_bounds=(1e05, 100000.0))
[source]
__call__(X, Y=None, eval_gradient=False)
[source]
Return the kernel k(X, Y) and optionally its gradient.
Parameters: 


Returns: 

bounds
Returns the logtransformed bounds on the theta.
Returns: 


clone_with_theta(theta)
[source]
Returns a clone of self with given hyperparameters theta.
Parameters: 


diag(X)
[source]
Returns the diagonal of the kernel k(X, X).
The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated.
Parameters: 


Returns: 

get_params(deep=True)
[source]
Get parameters of this kernel.
Parameters: 


Returns: 

hyperparameters
Returns a list of all hyperparameter specifications.
is_stationary()
[source]
Returns whether the kernel is stationary.
n_dims
Returns the number of nonfixed hyperparameters of the kernel.
set_params(**params)
[source]
Set the parameters of this kernel.
The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
Returns: 


theta
Returns the (flattened, logtransformed) nonfixed hyperparameters.
Note that theta are typically the logtransformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like lengthscales naturally live on a logscale.
Returns: 


sklearn.gaussian_process.kernels.RBF
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RBF.html